Tagged: Computational modeling

Accessing the Cortical Response to Macular Disease via a Large-Scale Spiking Neuron Model of V1

Purpose:: Current efforts for assessing macular disease have focused on the retina, for instance quantitation of drusen distributions. Retinal imaging, however, does not provide a complete picture of the nature of the expected vision loss. Important to consider is how the visual cortex responds to the resulting scotomata and distortion of the retinal input. Methods:: In this study we used an anatomically and physiologically detailed spiking neuron model of V1 (Wielaard and Sajda, Cerebral Cortex. 2006 16(11) 1531-1545) to investigate the effect of macular disease on cortical activity, tuning, and selectivity. We segmented fundus images and use them as “masks” for input to our cortical simulations. The model was probed using simulated drifting sinusoidal grating stimuli. All simulations were done using monocular input. We analyzed the firing rates and orientation selectivity of cells in parvocellular (4Cß) and magnocellular (4Cα) versions of the cortical model as a function of normal and abnormal retinal input. To analyze orientation selectivity we computed the circular variance (CV) across the population of cells. Results:: We found for the magnocellular model an overall reduction of firing rates of all cortical neurons. However there were no obvious “holes” of activity indicative of clusters of inactive neurons whose spatial position could be correlated with the spatial distribution of drusen. Analysis of orientation selectivity showed a dramatic reduction in selectivity for the normal vs abnormal cases. For the abnormal cases there was a shift of the CV distribution toward 1.0, indicating poorer orientation selectivity of the cells in 4Cα. For 4Cß the results are somewhat different. Unlike the magnocellular model, the parvocellular model showed clusters of inactivity which correlated with the spatial distribution of drusen. However the orientation selectivity was not significantly affected, with distributions between normal and abnormal cases being indistinguishable. Conclusions:: The magno system appears to fill-in spatial information though at the cost of a loss of orientation selectivity, were as the parvo system maintains orientation selectivity however with scotoma present in the cortical activity. This analysis is only “first order” in that drusen are treated purely as masking out the visual input, when in fact their effect on retinal ganglion cell activity can be more complex. Nonetheless, the simulations offer some insight into how responses of cortical neurons are affected by retinal disease.

Using a Spiking Neuron Model of V1 as a Substrate for Mapping Visual Stimuli to Perception

Purpose: : How visual stimuli map to neural activity and ultimately perception is important not only for understanding normal visual function but also for assessing how abnormalities and pathologies, for instance those arising in the retina, may ultimately affect perception. In this study we use a model of primary visual cortex (V1) as a substrate for mapping visual stimuli to a large population of neural activity and subsequently compare the accuracy of decoding this activity to the accuracy of human subjects for the same visual discrimination task. Methods: : We use a previously developed spiking neuron model of V1 as a recurrent network whose activity is consequently linearly decoded, providing a link to perception in the context of a visual discrimination task. We introduce a sparsity constraint in the decoder, given the hypothesis that information is sparsely distributed in a highly recurrent network of V1. A spatio-temporal word is constructed from the population spike trains, as input to the sparse decoder, to fully exploit the full dynamics of the model. We evaluate the decoding accuracy using a two alternative forced choice paradigm (face versus car discrimination) where we control the difficulty of the task by modulating the phase coherence in the images. We compare neurometric functions, constructed via the sparse decoding of the neural activity in the model, to psychometric functions obtained from 10 human subjects. Results: : In general, we find that relatively small fractions of the neurons are required for highly accurate decoding of the visual stimuli. We find that linear decoding of neural activity in a recurrent V1 model can yield discrimination accuracy that is at least as good as, if not better than, human psychophysical performance for relatively complex visual stimuli. Thus substantial information for super-accurate decoding remains at the level of V1 and loss of information needed to better match behavioral performance is predicted to occur downstream in the decision making process. We also find marginally better decoding accuracy by fully utilizing the spatial-temporal dynamics compared with a static decoding strategy. Conclusions: : We have demonstrated how we can link the visual stimulus to perception via a mapping through a spiking neuron model of the early visual system. Future work will consider this as a framework for potentially analyzing the perceptual effect of retinal vision loss in patients with mild yet progressive macular disease, comparing predictions to those obtained strictly from the analysis of the spatial distribution of retinal abnormalities such as drusen.

Perceptual Consequences of Macular Disease Evaluated Using a Model of V1

Purpose: : Clinical assessment of macular disease typically relies on direct analysis of retinal imaging, which does not necessarily provide a complete picture of expected vision loss. A potential advancement is a framework for predicting how retinal disease affects cortical activity and ultimately perceptual performance. Methods: : Fundus images for low-vision patients with macular disease were segmented to create masks, used to simulate disease-specific distortion at the level of the retina. A 2-AFC perceptual task was designed with the goal to discriminate face and car images in the presence of noise. 10 subjects with normal vision performed the task and their results were assessed via psychometric curves. We simulated the cortical activity given the stimuli and used linear decoding of spike trains to generate neurometric curves for the model. The sparse linear decoder was optimized to maximize discrimination and not to match subjects’ psychometric curves. We simulated the cortical activity of low-vision subjects using the mask-distorted stimuli and carried out the decoding analysis in the same manner as normal subjects. Results: : Shown are the mean psychometric curve for normal subjects (red), individual subjects (light red), mean neurometric curve for simulated “normal” subjects (black), and a simulated “low-vision” subject (gray). The mean simulated “normal” subject has a neurometric curve that is a reasonable match to normal subjects, for the most part falling within the inter-subject variation. For the simulated “low vision” case, the neurometric curve is shifted to the right indicating degradation in perceptual performance. Conclusions: : Our results are promising in that they predict healthy subject perceptual performance and also result in systematic shifts in performance for simulated “low-vision” cases. Future work will quantify the predictive value of the model for a population of low-vision patients.

Analysis of a gain control model of V1: Is the goal redundancy reduction?

In this paper we analyze a popular divisive normalization model of V1 with respect to the relationship between its underlying coding strategy and the extraclassical physiological responses of its constituent modeled neurons. Specifically we are interested in whether the optimization goal of redundancy reduction naturally leads to reasonable neural responses, including reasonable distributions of responses. The model is trained on an ensemble of natural images and tested using sinusoidal drifting gratings, with metrics such as suppression index and contrast dependent receptive field growth compared to the objective function values for a sample of neurons. We find that even though the divisive normalization model can produce “typical” neurons that agree with some neurophysiology data, distributions across samples do not agree with experimental data. Our results suggest that redundancy reduction itself is not necessarily causal of the observed extraclassical receptive field phenomena, and that additional optimization dimensions and/or biological constraints must be considered.

Simulated optical imaging of orientation preference in a model of V1

Optical imaging studies have played an important role in mapping the orientation selectivity and ocular dominance of neurons across an extended area of primary visual cortex (V1). Such studies have produced images with a more or less smooth and regular spatial distribution of relevant neuronal response properties. This is in spite of the fact that results from electrophysiological recordings, though limited in their number and spatial distribution, show significant scatter/variability in the relevant response properties of nearby neurons. In this paper we present a simulation of the optical imaging experiments of ocular dominance and orientation selectivity using a computational model of the primary visual cortex. The simulations assume that the optical imaging signal is proportional to the averaged response of neighboring neurons. The model faithfully reproduces ocular dominance columns and orientation pinwheels in the presence of realistic scatter of single cell preferred responses. In addition,we find the simulated optical imaging of orientation pinwheels to be remarkably robust, with the pinwheel structure maintained up to an addition of degrees of random scatter in the orientation preference of single cells. Our results suggest that an optical imaging result does not necessarily, by itself, provide any obvious upperbound for the scatter of the underlying neuronal response properties on local scales.